Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rp1-run/rp1 --skill deep-researchgit clone --depth 1 https://github.com/rp1-run/rp1Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rp1-run/rp1/deep-research)<a href="https://agentmods.dev/skills/rp1-run/rp1/deep-research"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/deep-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rp1-run/rp1/deep-research"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.02914 |
| Opus 5 | $0.00013 | $0.01457 |
| Sonnet 5 | $0.00005 | $0.00583 |
| Haiku 4.5 | $0.00003 | $0.00291 |
Grade A, and why
deep-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research - Orchestration Command
You are executing the Deep Research workflow. You coordinate autonomous research through a map-reduce architecture: clarify intent, spawn parallel explorers, synthesize findings, and delegate report generation.
CRITICAL: Commands CAN spawn agents. You will spawn research-explorer agents for exploration and research-reporter for report generation. Do NOT delegate orchestration to another agent.
STATE-MACHINE
stateDiagram-v2
[*] --> clarify
clarify --> plan : intent_clear
plan --> explore : plan_ready
explore --> synthesize : exploration_complete
synthesize --> report : synthesis_complete
report --> [*] : done
On each phase transition, report via:
rp1 agent-tools emit \
--workflow deep-research \
--type status_change \
--run-id {RUN_ID} \
--name "Research: {brief summary of research topic}" \
--step {CURRENT_STATE} \
--data '{"status": "running"}'
RUN_IDcomes from the generated Workflow Bootstrap section
State Progression Protocol:
- Report each
--stepwith--data '{"status": "running"}'when you enter that state - For non-terminal states: move to the NEXT state when done (entering the next state implies the previous completed)
- For terminal states (those with
→ [*]transitions): report with--data '{"status": "completed"}'and--close-runwhen the step's work finishes - On error, transition to the appropriate failure state in the graph
Example sequence:
--step clarify --data '{"status": "running"}' # entering clarify phase
--step plan --data '{"status": "running"}' # intent clear, entering plan phase
--step explore --data '{"status": "running"}' # plan ready, entering explore phase
--step synthesize --data '{"status": "running"}' # exploration done, entering synthesize phase
--step report --data '{"status": "running"}' # synthesis done, entering report phase
--step report --data '{"status": "completed"}' --close-run # report work finished, workflow done
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 378 lines · 25 tokens per session scan A b35f97e50b5e
deep-research is a skill published in the GitHub repository rp1-run/rp1 (38 stars, last pushed 2d ago), licensed Apache-2.0. It adds 25 tokens to every session and 2,914 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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